PSSA : parallel stretched simulated annealing

We consider the problem of finding all the global (and some local) minimizers of a given nonlinear optimization function (a class of problems also known as multi-local programming problems), using a novel approach based on Parallel Computing. The approach, named Parallel Stretched Simulated Annealing (PSSA), combines simulated annealing with stretching function technique, in a parallel execution environment. Our PSSA software allows to increase the resolution of the search domains (thus facilitating the discovery of new solutions) while keeping the search time bounded. The software was tested with a set of well known problems and some numerical results are presented

Application of the stretched simulated annealing method in the stability analysis of multicomponent systems using excess gibbs energy models

In this work, the Stretched Simulated Annealing Method was applied to identify the stationary points of the tangent plane distance function defined for the Gibbs energy. The classic excess Gibbs energy Non Random Two Liquid model was used for these studies in several multicomponent mixtures, for which specific numerical difficulties were shown. The results obtained by applying the methodology developed in this work were very satisfactory

Wireless Energy Harvesting with Amplify-and-Forward Relaying and Link Adaptation under Imperfect Feedback Channel

Energy harvesting is an alternative approach to extend the lifetime of wireless communications and decrease energy consumption, which results in fewer carbon emissions from wireless networks. In this study, adaptive modulation with EH relay is proposed. A power splitting mechanism for EH relay is used. The relay harvests energy from the source and forwards the information to the destination. A genetic algorithm (GA) is applied for the optimisation of the power splitting ratio at the relays. Two scenarios are considered namely, perfect and imperfect feedback channels. Results show that the spectral efficiency (SE) degradation, which is due to an imperfect feedback channel, was approximately 14% for conventional relays. The use of energy harvesting results in a degradation in the performance of SE of approximately 19% in case of a perfect feedback channel. Finally, an increase in the number of energy harvesting relays enhances the SE by 22%

A new approach to particle swarm optimization algorithm

Particularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorithms. These studies concern the particle swarm optimization algorithm as a model of predator and prey. New properties of the algorithm resulting from the co-operation mechanism that determines the operation of algorithm and significantly reduces environmental influence have been shown. Definitions of functions of behavior scenarios give new feature of the algorithm. This feature allows self controlling the optimization process. This approach can be successfully used in computer games. 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Application of Meta-Heuristic Algorithms in Reservoir Supply Optimization, Case Study: Mahabad Dam in Iran

In arid and semi-arid areas, optimization and strategic planning of water delivery through an optimal and intelligently designed reservoir supply system is a primary task for water resources management. In this regard, the election algorithm (EA) is presented to estimate the optimal storage capacity of the Mahabad dam located in northwest Iran. EA is an intelligent iterative population-based algorithm that has recently been introduced for dealing with different optimization purposes. The capability of EA to address issues of local minimums in the feature search space is employed to yield a globally optimal explanation of the present issue. The data used in this study comprise 7-year (2008-2015) evaporation, rainfall, reservoir storage, reservoir inflows, and outflow. The results obtained from the EA approach are approximated with the continuous genetic algorithm (CGA). Based on the estimated results in the testing phase, an average relatively error (5.65%) is attained in the last implementation of the algorithm. The high efficacy of EA relative to the benchmark models in terms of the NSE and RMSE, MAE is found to be approximately 0.037, 0.41, and 0.74, respectively, which are less than the values of these criteria for the CGA. These error measures, i.e. NSE, MAE, and RMSE, for the CGA were calculated to be 0.66, 0.56, and 0.042, respectively. The obtained accurate results show the high performance of the EA model in estimating the optimal reservoir capacity and its efficiency in water resources management